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Design of Concentric Circular Antenna Array with Central Element Feeding Using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach and evolutionary Programing Technique

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JOURNAL OF INFRARED MILLIMETER AND TERAHERTZ WAVES 2010年第6期31卷 667-680页

作者： Mandal, Durbadal Ghoshal, Sakti Prasad Bhattacharjee, Anup Kumar Natl Inst Technol Dept Elect & Commun Engn Durgapur 713209 W Bengal India Natl Inst Technol Dept Elect Engn Durgapur 713209 W Bengal India

In this paper the maximum sidelobe level (SLL) reductions without and with central element feeding in various designs of three-ring concentric circular antenna arrays (CCAA) are examined using a real-coded evolutionary programming (EP) to finally determine the global optimal three-ring CCAA design. Standard real-coded Particle Swarm Optimization (PSO) and real-coded Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSOCFIWA) are also employed for comparative optimization but both prove to be suboptimal. This paper assumes non-uniform excitation weights and uniform spacing of excitation elements in each three-ring CCAA design. Among the various CCAA designs, the design containing central element and 4, 6 and 8 elements in three successive concentric rings proves to be such global optimal design set with global minimum SLL (-39.66 dB) as determined by evolutionary programming.

关键词： Concentric circular antenna array Non-uniform excitation Sidelobe level Standard particle swarm optimization Particle swarm optimization with constriction factor and inertia weight approach evolutionary programming First null beamwidth

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Multi-objective Optimal Power Flow Using Biogeography-based Optimization

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2010年第12期38卷 1406-1426页

作者： Roy, P. K. Ghoshal, S. P. Thakur, S. S. Natl Inst Technol Dept Elect Engn Durgapur W Bengal India

This article presents a novel biogeography-based optimization algorithm for solving constrained optimal power flow problems in power systems, considering valve point non-linearities of generators. In this article, the feasibility of the proposed algorithm is demonstrated for 9-bus, 26-bus, and IEEE 118-bus systems with three different objective functions, and it is compared to other well-established population-based optimization techniques. A comparison of simulation results reveals better solution quality and computational efficiency of the proposed algorithm over evolutionary programming, genetic algorithm, and mixed-integer particle swarm optimization for the global optimization of multi-objective constrained optimal power flow problems.

关键词： biogeography optimal power flow particle swarm optimization genetic algorithm evolutionary programming mutation migration

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Convergence guarantees for generalized adaptive stochastic search methods for continuous global optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第3期207卷 1187-1202页

作者： Regis, Rommel G. St Josephs Univ Dept Math Philadelphia PA 19131 USA

This paper presents some simple technical conditions that guarantee the convergence of a general class of adaptive stochastic global optimization algorithms. By imposing some conditions on the probability distributions that generate the iterates, these stochastic algorithms can be shown to converge to the global optimum in a probabilistic sense. These results also apply to global optimization algorithms that combine local and global stochastic search strategies and also those algorithms that combine deterministic and stochastic search strategies. This makes the results applicable to a wide range of global optimization algorithms that are useful in practice. Moreover, this paper provides convergence conditions involving the conditional densities of the random vector iterates that are easy to verify in practice. It also provides some convergence conditions in the special case when the iterates are generated by elliptical distributions such as the multivariate Normal and Cauchy distributions. These results are then used to prove the convergence of some practical stochastic global optimization algorithms, including an evolutionary programming algorithm. In addition, this paper introduces the notion of a stochastic algorithm being probabilistically dense in the domain of the function and shows that, under simple assumptions, this is equivalent to seeing any point in the domain with probability 1. This, in turn, is equivalent to almost sure convergence to the global minimum. Finally, some simple results on convergence rates are also proved. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Global optimization Stochastic search Random search Convergence evolutionary algorithm evolutionary programming

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Short term scheduling of multiple grid-parallel PEM fuel cells for microgrid applications

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2010年第20期35卷 11099-11106页

作者： El-Sharkh, M. Y. Rahman, A. Alam, M. S. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

This paper presents a short term scheduling scheme for multiple grid-parallel PEM fuel cell power plants (FCPPs) connected to supply electrical and thermal energy to a microgrid community. As in the case of regular power plants, short term scheduling of FCPP is also a cost-based optimization problem that includes the cost of operation, thermal power recovery, and the power trade with the local utility grid. Due to the ability of the microgrid community to trade power with the local grid, the power balance constraint is not applicable, other constraints like the real power operating limits of the FCPP, and minimum up and down time are therefore used. To solve the short term scheduling problem of the FCPPs, a hybrid technique based on evolutionary programming (EP) and hill climbing technique (HC) is used. The EP is used to estimate the optimal schedule and the output power from each FCPP. The HC technique is used to monitor the feasibility of the solution during the search process. The short term scheduling problem is used to estimate the schedule and the electrical and thermal power output of five FCPPs supplying a maximum power of 300 kW. (C) 2010 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved.

关键词： PEM fuel cell Short term scheduling evolutionary programming Hill climbing Distributed generation Unit commitment

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Economics of hydrogen production and utilization strategies for the optimal operation of a grid-parallel PEM fuel cell power plant

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2010年第16期35卷 8804-8814页

作者： El-Sharkh, M. Y. Tanrioven, M. Rahman, A. Alam, M. S. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA Yildiz Tech Univ Dept Elect Engn TR-34349 Istanbul Turkey

This paper integrates the hydrogen production and utilization strategies with an economic model of a PEM fuel cell power plant (FCPP). The model includes the operational cost, thermal recovery, power trade with the local grid, and hydrogen management strategies. The model is used to determine the optimal operational strategy, which yields the minimum operating cost. The optimal operational strategy is achieved through estimation of the following: hourly generated power, thermal power recovered from the FCPP, power trade with the local grid, and hydrogen production. An evolutionary programming-based technique is used to solve for the optimal operational strategy. The model is tested using different seasonal load demands. The results illustrate the impact of hydrogen management strategies on the operational cost of the FCPP when subjected to seasonal load variation. Results are encouraging and indicate viability of the proposed model. (C) 2010 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved.

关键词： Fuel cell Hydrogen PEM fuel cell Economical fuel cell model evolutionary programming

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A real-coded biogeography-based optimization with mutation

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APPLIED MATHEMATICS AND COMPUTATION 2010年第9期216卷 2749-2758页

作者： Gong, Wenyin Cai, Zhihua Ling, Charles X. Li, Hui China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Univ Western Ontario Dept Comp Sci London ON N6A 5B7 Canada

Biogeography-based optimization (BBO) is a new biogeography inspired algorithm for global optimization. There are some open research questions that need to be addressed for BBO. In this paper, we extend the original BBO and present a real-coded BBO approach, referred to as RCBBO, for the global optimization problems in the continuous domain. Furthermore, in order to improve the diversity of the population and enhance the exploration ability of RCBBO, the mutation operator is integrated into RCBBO. Experiments have been conducted on 23 benchmark problems of a wide range of dimensions and diverse complexities. The results indicate the good performance of the proposed RCBBO method. Moreover, experimental results also show that the mutation operator can improve the performance of RCBBO effectively. Crown Copyright (C) 2010 Published by Elsevier Inc. All rights reserved.

关键词： Biogeography-based optimization Mutation Global optimization evolutionary programming Exploration ability

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Current reference generator for 50-Hz and 16.7-Hz shunt active power filters

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INTERNATIONAL JOURNAL OF ELECTRONICS 2010年第1期97卷 63-81页

作者： Komrska, Tomas Zak, Jan Ovaska, Seppo J. Peroutka, Zdenek Univ W Bohemia Fac Elect Engn Dept Electromech & Power Elect Plzen 30614 Czech Republic Aalto Univ Fac Elect Commun & Automat Inst Intelligent Power Elect FIN-02150 Espoo Finland

This article describes a high-performance reference signal generator for shunt-type active power filters in frequency-variant environments. First, it extracts the fundamental frequency component from the distorted load current. The actual current reference is obtained by subtracting the recofundamental harmonic from the measured load current, and inverting the resulting harmonics waveform before feeding it to a current-controlled IGBT inverter section. To achieve a high-quality fundamental sinusoid estimation and real-time computational efficiency, our reference generator employs an adaptive and predictive multiplicative general parameter finite impulse response (MGP-FIR) band pass filter designed by evolutionary programming. Detailed procedures of MGP-FIR filtering and evolutionary optimisation are discussed. Presented theoretical conclusions are verified by simulation results and experiments performed on a designed laboratory prototype of the single-phase active power filter of rated power of 5.75 kVA. This is the first article reporting any kind of prototyping results with the MGP-FIR system.

关键词： active power filter current reference generator adaptive filtering predictive filtering evolutionary programming

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Nature-inspired techniques for conformance testing of object-oriented software

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APPLIED SOFT COMPUTING 2010年第3期10卷 730-745页

作者： Bouchachia, A. Mittermeir, R. Sielecky, P. Stafiej, S. Zieminski, M. Univ Klagenfurt Grp Software Engn & Soft Comp Dept Informat Syst A-9020 Klagenfurt Austria

Soft computing offers a plethora of techniques for dealing with hard optimization problems. In particular, nature based techniques have been shown to be very efficient in optimization applications. The present paper investigates the suitability of various nature-inspired meta-heuristics (genetic algorithms, evolutionary programming and ant-colony systems) to the problem of software testing. The present study is part of the nature-inspired techniques for object-oriented testing (NITOT) environment. It aims at addressing the problem of conformance testing of object-oriented software to its specification expressed in terms of finite state machines. Detailed description, adaptation and evaluation of the various nature-inspired meta-heuristics are discussed showing their potential in this context of conformance testing. (C) 2009 Elsevier B. V. All rights reserved.

关键词： Conformance testing Testing data generation Genetic algorithms evolutionary programming Ant-colony systems

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evolutionary Fuzzy ARTMAP Neural Networks and their Applications to Fault Detection and Diagnosis

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NEURAL PROCESSING LETTERS 2010年第3期31卷 219-242页

作者： Tan, Shing Chiang Lim, Chee Peng Multimedia Univ Fac Informat Sci & Technol Bukit Beruang 75450 Melaka Malaysia Univ Sci Malaysia Sch Elect & Elect Engn Nibong Tebal 14300 Penang Malaysia

In this paper, two mutation-based evolving artificial neural networks, which are based on the Fuzzy ARTMAP (FAM) network and evolutionary programming, are proposed. The networks utilize the knowledge base extracted from a set of data to perform search and adaptation. The performances of the two networks are assessed using benchmark problems, with the results analyzed and discussed. The effects of the network parameters are evaluated through a parametric study. The applicability of the networks is also demonstrated using a real fault detection and diagnosis task in a power generation plant. The experimental results consistently indicate the usefulness of the proposed evolutionary FAM-based networks in yielding good classification performances with parsimonious network structures.

关键词： Fuzzy ARTMAP Dynamic decay adjustment evolutionary programming Fault detection and diagnosis

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Neurocontrol of a multi-effect batch distillation pilot plant based on evolutionary reinforcement learning

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CHEMICAL ENGINEERING SCIENCE 2010年第5期65卷 1627-1643页

作者： Conradie, Alex V. E. Aldrich, Chris Univ Stellenbosch Dept Proc Engn ZA-7602 Stellenbosch South Africa

The time cost of first-principles dynamic modelling and the complexity of nonlinear control strategies may limit successful implementation of advanced process control. The maximum return on fixed capital within the processing industries is thus compromised. This study introduces a neurocontrol methodology that uses partial system identification and symbiotic memetic neuro-evolution (SMNE) for the development of neurocontrollers. Partial system identification is achieved using singular spectrum analysis (SSA) to extract state variables from time series data. The SMNE algorithm uses a symbiotic evolutionary algorithm and particle swarm optimisation to learn optimal neurocontroller weights from the partially identified system within a reinforcement learning framework. A multi-effect batch distillation (MEBAD) pilot plant was constructed to demonstrate the real world application of the neurocontrol methodology, motivated by the nonsteady state operation and nonlinear process interaction between multiple distillation columns. Multi-loop proportional integral (PI) control was implemented as a reduced model, reflecting an approach involving no modelling or significant controller tuning. Rapid multiple input multiple out nonlinear controller development was achieved using SSA and the SMNE algorithm, demonstrating comparable time and cost to implementation in relation to the reduced model. The optimal neurocontroller reduced the batch time and therefore the energy consumption by 45% compared to conventional multi-loop SISO PI control. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Distillation Process control Nonlinear dynamics Mathematical modelling Neural networks evolutionary programming

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